Continuous-mode quantum key distribution with digital signal processing

Chen, Ziyang; Wang, Xiangyu; Yu, Song; Li, Zhengyu; Guo, Hong

doi:10.1038/s41534-023-00695-8

Cited by 39 publications

(15 citation statements)

References 47 publications

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“…For solving tensor equations with applications, Liang, Zheng, and Zhao proposed alternating iterative methods based on ADMM, such as G-ADMM (Gauss–Seidel scheme) and TT-ADMM (tensor–train) [ 45 ]. Chen et al provided a new idea to compute tensor eigenvalues by using digital signal processing technology and proved its feasibility from the perspective of a continuous-mode system [ 46 ].…”

Section: Tensor Eigenvalue Calculationmentioning

confidence: 99%

Spectrum Situation Awareness for Space–Air–Ground Integrated Networks Based on Tensor Computing

Qi,

Zhang,

Zhang

2024

Sensors

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The spectrum situation awareness problem in space–air–ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space–air–ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.

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Section: Tensor Eigenvalue Calculationmentioning

confidence: 99%

Spectrum Situation Awareness for Space–Air–Ground Integrated Networks Based on Tensor Computing

Qi,

Zhang,

Zhang

2024

Sensors

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“…Our implementation, based on the architecture described in [30], features a single hidden layer of LSTM composed of 80 cells. We maintain a consistent configuration with 2 15 sequences consisting of 256 elements, each of which represents one byte of the random sequence. The training spans 100 iterations, with a learning rate of 0.01 and a minibatch size of 250.…”

Section: Proposed Framework Implementationmentioning

confidence: 99%

“…This is shown for instance in the comparison of the baseline results for the LCG with the results obtained with CNN-1 for the same generator. Results in table 3 shows that increasing the training set size from 2 15 to 2 18 and changing LSTM by CNN improves the capability of the NN to discriminate between those generators and the GSRNG since perfect discrimination is achieved when AO.PRNG and AO.GSRNG are 1 and 0, respectively. VCSEL QRNG was indistinguishable even with the biggest networks that we have tried, as shown in the last three rows of table 3, since a value of 0.5 for AO.PRNG and AO.GSRNG means that the NN is unable to discriminate between VCSEL QRNG and GSRNG.…”

Section: Network Design and Parameters Influencementioning

confidence: 99%

“…These QRNGs are widely used in current real-world Quantum Key Distribution (QKD) systems [9]. Continuous-variable QKD that encodes the information on the quadratures of the lasing field is a strong alternative to discrete-variable QKD because it can be implemented using the current coherent optical fiber technology and because it does not require single-photon detectors [13][14][15][16][17]. QRNGs based on gain-switching of an special type of semiconductor laser, the vertical-cavity surface-emitting laser (VCSEL), have also been recently demonstrated [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the quality of random number generators through neural networks

Luis Crespo,

González-Villa,

Gutiérrez

et al. 2024

Mach. Learn.: Sci. Technol.

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In this paper we address the use of Neural Networks (NNs) for the assessment of the quality and hence safety of several Random Number Generators (RNGs), focusing both on the vulnerability of classical Pseudo Random Number Generators (PRNGs), such as Linear Congruential Generators (LCGs) and the RC4 algorithm, and extending our analysis to non-conventional data sources, such as Quantum Random Number Generators (QRNGs) based on Vertical-Cavity Surface-Emitting Laser (VCSEL). Among the results found, we have classified the generators based on the capability of the NN to distinguish between the RNG and a Golden Standard RNG (GSRNG). We show that sequences from simple PRNGs like LCGs and RC4 can be distinguished from the GSRNG. We also show that sequences from LCG on elliptic curves and VCSEL-based QRNG can not be distinguished from the GSRNG even with the biggest long-short term memory or convolutional neural networks (CNNs) that we have considered. We underline the fundamental role of design decisions in enhancing the safety of RNGs. The influence of network architecture design and associated hyper-parameters variations was also explored. We show that longer sequence lengths and CNNs are more effective for discriminating RNGs against the GSRNG. Moreover, in the prediction domain, the proposed model is able to deftly distinguish between the raw data of our QRNG and data from the GSRNG exhibiting a cross-entropy error of 0.52 on the test data-set used. All these findings reveal the potential of NNs to enhance the security of RNGs, while highlighting the robustness of certain QRNGs, in particular the VCSEL-based variants, for high-quality random number generation applications.

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“…To put it simply, an object's properties between two or more objects are dependent to each other. Over the years, quantum entanglement has sparked a massive interest of research in numerous fields namely quantum computing, quantum cryptography and quantum teleportation [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Entanglement Classification for Three-qubit Pure Quantum System using Special Linear Group under the SLOCC Protocol

Zhahir,

Mohd,

Shuhud

et al. 2023

IJACSA

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Quantum technology has been introduced in the IR 4.0, breeding a new era of advanced technology revolutionizing the future. Hence, understanding the key resources of quantum technology, quantum entanglement is vital. Growing interests in quantum technologies has raised comprehensive studies of quantum entanglement, especially on the entanglement classification. Special Linear group, SL(n) of multipartite entanglement classification under the SLOCC protocol is not widely studied due to its complex structure, creating a curb in developing its classification method. Therefore, this paper developed and delivered a classification method of pure multipartite, three-qubit quantum state using a combination of Special Linear group, SL(2) x SL(2) x SL(2) model operator under the SLOCC, classifying entanglement using the model operator with certain selected parameters. Further analysis was done resulting in the determination of the six subgroups, namely fully separable (A-B-C), bi-separable (A-BC, B-AC and C-AB) and genuinely entangled (W and GHZ).

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Continuous-mode quantum key distribution with digital signal processing

Cited by 39 publications

References 47 publications

Spectrum Situation Awareness for Space–Air–Ground Integrated Networks Based on Tensor Computing

Spectrum Situation Awareness for Space–Air–Ground Integrated Networks Based on Tensor Computing

Assessing the quality of random number generators through neural networks

Entanglement Classification for Three-qubit Pure Quantum System using Special Linear Group under the SLOCC Protocol

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